Imaging modalities such as computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, positron emission tomography (PET) and single-photon emission computed tomography (SPECT) provide various representations of anatomical features of a subject. Image sets generated with any of these modalities can be used for diagnosis or to guide various treatments, such as surgery or radiation therapy. The images can consist of two-dimensional representations, three-dimensional voxel images, or a series of temporal three-dimensional images. It is often preferable to contour or segment organs or lesions within the images, thus allowing calculation of volumes, improved visualization, and more accurate treatment planning. It also facilitates the modification of treatments for image-guided surgery or radiotherapy.
However, because of the complexity of these systems and various characteristics of the resulting images, interpretation typically requires a skilled and highly-trained physician. In one conventional approach, for example, images are segmented by an individual (such as the physician) by using a pointing device (e.g., a mouse) to select various points on the surface of an organ, or by electronically “painting” the image using a paintbrush tool. Three-dimensional images can be contoured by repeating the process on various two-dimensional slices throughout the organ to create a three-dimensional surface. The process, however, is time-consuming and prone to user variability.
Many automatic segmentation algorithms have been disclosed in the literature, and are familiar to those practicing the art. Each is usually adapted to a particular imaging modality or organ type, with varying success. In particular, medical ultrasound images are intrinsically difficult for segmentation algorithms. Organ boundaries can be masked by the presence of speckle noise; parts of the boundary may appear weak due to shading by overlying features; and edges can be formed by two regions of different gray levels or as the edge between two different textures, or as a hybrid of the two. This complexity leads to high failure rates for conventional automatic segmentation algorithms. A fast and robust automatic segmentation algorithm, which acts on two-dimensional or three-dimensional images, is therefore needed.